Gram-Schmidt orthogonalization: 100 years and more
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Publication:2931519
DOI10.1002/nla.1839zbMath1313.65086OpenAlexW1859757340MaRDI QIDQ2931519
Steven J. Leon, Åke Björck, Walter Gander
Publication date: 25 November 2014
Published in: Numerical Linear Algebra with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/nla.1839
algorithmerror analysishistorical surveyorthogonalizationleast squaresKrylov subspace methodsQR factorizationsurvey articleGram-Schmidt
Research exposition (monographs, survey articles) pertaining to numerical analysis (65-02) Iterative numerical methods for linear systems (65F10) History of numerical analysis (65-03) Orthogonalization in numerical linear algebra (65F25)
Related Items (21)
Fermionic quantum orthogonalizations. I ⋮ Properties of a method of fundamental solutions for the parabolic heat equation ⋮ A Note on Inexact Inner Products in GMRES ⋮ Householder Orthogonalization with a Nonstandard Inner Product ⋮ A simple and practical representation of compatibility condition derived using a \textbf{QR} decomposition of the deformation gradient ⋮ An Efficient Algorithm for Integer Lattice Reduction ⋮ Practical algorithms for multivariate rational approximation ⋮ Ultra-high dimensional variable screening via Gram-Schmidt orthogonalization ⋮ GMRES algorithms over 35 years ⋮ Cholesky and Gram-Schmidt Orthogonalization for Tall-and-Skinny QR Factorizations on Graphics Processors ⋮ Analysis of the self projected matching pursuit algorithm ⋮ Laplace stretch: Eulerian and Lagrangian formulations ⋮ Characterizing geometrically necessary dislocations using an elastic-plastic decomposition of Laplace stretch ⋮ An online incremental orthogonal component analysis method for dimensionality reduction ⋮ An approach of orthogonalization within the Gram-Schmidt algorithm ⋮ A decomposition of Laplace stretch with applications in inelasticity ⋮ Functional variable selection via Gram–Schmidt orthogonalization for multiple functional linear regression ⋮ Numerical solution of 2 × 2 block linear systems by block Gram–Schmidt methods ⋮ Optimal orthogonalization processes ⋮ Block Gram-Schmidt algorithms and their stability properties ⋮ Randomized Gram--Schmidt Process with Application to GMRES
Uses Software
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